The improvement of high-performance computer architectures, plus the availability of data from numerical simulations and experiments, sparked an increase in interest to use machine learning methods to solve problems in many scientific disciplines. This has led to label machine learning as the fourth paradigm in science, next to experimentation, theory and simulation [1]}. When it comes to fluid mechanics, applying machine learning methods constitutes a challenge for several reasons, such as the transient nature of most fluid mechanics problems, the heterogeneity of most available data, the extensive non-linearities that govern fluid mechanics, and the multi-scale nature of most problems in hand [2]}. To deal with these challenges, an ideal machine learning algorithm for fluid mechanics, should possess features such as interpretability, explainability, generalisability, and convergence [2]}. One of the most popular machine learning frameworks that are used extensively in different fluid mechanics problems, from solving partial differential equations [4]}, [5]}, discovering physics [6]}, learning the active-nematic hydrodynamics [7]}, and predicting physical properties [8]} are the artificial neural networks (ANN). Other machine learning methods that are used for scientific discovery are sparse identification of nonlinear dynamical systems for discovering differential equations from sparse data [9]}, and symbolic regression that is used for discovering laws of nature [10]}, discovering new materials [11]}, and solving
fluid flow problems [12]}.
